Many applied statisticians, particularly those interested in estimating
causal effects using linear models, prize unbiased parameter estimation
and are willing to sacrifice on other fronts to achieve it.
For example, the standard OLS estimator (which is the minimum-variance unbiased estimator
under the assumptions of the Gauss-Markov theorem) is used for estimating parameters
of a linear regression model in favor of other biased estimators, such as
the James-Stein estimator [1]}, [2]}, which dominate it
in terms of \(\ell _2\)  error of the parameter estimates.
